Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,712
2	New Jersey	19,784
3	Rhode Island	16,192
4	Massachusetts	16,008
5	District of Columbia	14,975
6	Louisiana	14,707
7	Arizona	14,447
8	Connecticut	13,191
9	Delaware	12,748
10	Illinois	11,845
11	Maryland	11,741
12	Mississippi	10,824
13	Nebraska	10,445
14	Iowa	10,190
15	Florida	9,953
16	Alabama	9,337
17	South Carolina	9,196
18	Georgia	8,825
19	Utah	8,173
20	Arkansas	8,122
21	South Dakota	8,096
22	Virginia	7,819
23	Nevada	7,742
24	Tennessee	7,708
25	Texas	7,558
26	Pennsylvania	7,497
27	Michigan	7,410
28	Indiana	7,406
29	California	7,282
30	North Carolina	7,270
31	Minnesota	6,945
32	New Mexico	6,546
33	Wisconsin	6,154
34	Colorado	6,028
35	Kansas	5,889
36	Washington	5,129
37	North Dakota	5,121
38	Ohio	5,039
39	Idaho	4,802
40	New Hampshire	4,362
41	Oklahoma	4,351
42	Missouri	4,165
43	Kentucky	3,988
44	Wyoming	2,956
45	Puerto Rico	2,728
46	Maine	2,559
47	Oregon	2,516
48	Vermont	2,009
49	West Virginia	1,955
50	Alaska	1,945
51	Montana	1,241
52	Hawaii	743

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	482
2	Florida	368
3	Louisiana	356
4	Texas	269
5	South Carolina	256
6	Nevada	235
7	Idaho	214
8	Alabama	198
9	Georgia	191
10	California	190
11	Mississippi	172
12	Tennessee	166
13	Utah	154
14	Arkansas	143
15	Delaware	143
16	North Carolina	143
17	Oklahoma	132
18	Kansas	113
19	New Mexico	105
20	Iowa	104
21	Washington	99
22	Puerto Rico	96
23	Wisconsin	91
24	Minnesota	89
25	Kentucky	82
26	Missouri	81
27	Ohio	77
28	Maryland	69
29	Nebraska	65
30	Virginia	63
31	Indiana	60
32	Wyoming	60
33	District of Columbia	57
34	West Virginia	55
35	Oregon	53
36	Pennsylvania	53
37	Illinois	51
38	Rhode Island	51
39	North Dakota	50
40	South Dakota	50
41	Montana	49
42	Colorado	47
43	Michigan	43
44	Alaska	38
45	New Jersey	31
46	Connecticut	29
47	New York	28
48	Massachusetts	24
49	Hawaii	16
50	New Hampshire	14
51	Maine	10
52	Vermont	8

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,720
2	New York	1,641
3	Connecticut	1,216
4	Massachusetts	1,191
5	Rhode Island	914
6	District of Columbia	794
7	Louisiana	713
8	Michigan	626
9	Illinois	574
10	Maryland	540
11	Pennsylvania	533
12	Delaware	527
13	Indiana	403
14	Mississippi	389
15	Colorado	296
16	New Hampshire	282
17	Georgia	268
18	Minnesota	268
19	Arizona	265
20	Ohio	254
21	New Mexico	247
22	Iowa	232
23	Virginia	220
24	Alabama	210
25	Washington	181
26	Florida	178
27	Nevada	178
28	Missouri	176
29	California	166
30	South Carolina	164
31	Nebraska	148
32	Kentucky	141
33	Wisconsin	140
34	North Carolina	137
35	North Dakota	116
36	South Dakota	110
37	Oklahoma	102
38	Arkansas	99
39	Kansas	99
40	Texas	97
41	Tennessee	96
42	Vermont	89
43	Maine	81
44	Utah	60
45	West Virginia	53
46	Idaho	52
47	Oregon	52
48	Puerto Rico	49
49	Wyoming	36
50	Montana	21
51	Alaska	20
52	Hawaii	13

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	5
2	Mississippi	5
3	New Jersey	3
4	Florida	2
5	Louisiana	2
6	Nevada	2
7	Rhode Island	2
8	South Carolina	2
9	Alabama	1
10	Arkansas	1
11	California	1
12	District of Columbia	1
13	Georgia	1
14	Illinois	1
15	Indiana	1
16	Iowa	1
17	Kentucky	1
18	Maryland	1
19	Massachusetts	1
20	Michigan	1
21	Missouri	1
22	New York	1
23	North Carolina	1
24	Ohio	1
25	Pennsylvania	1
26	Tennessee	1
27	Texas	1
28	Utah	1
29	Virginia	1
30	Washington	1
31	Alaska	0
32	Colorado	0
33	Connecticut	0
34	Delaware	0
35	Hawaii	0
36	Idaho	0
37	Kansas	0
38	Maine	0
39	Minnesota	0
40	Montana	0
41	Nebraska	0
42	New Hampshire	0
43	New Mexico	0
44	North Dakota	0
45	Oklahoma	0
46	Oregon	0
47	Puerto Rico	0
48	South Dakota	0
49	Vermont	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	133,197	1	99
Lake	Tennessee	98,917	2	99
Lee	Arkansas	90,889	3	99
Dakota	Nebraska	89,733	4	99
Buena Vista	Iowa	87,615	5	99
Davidson	Tennessee	17,795	156	95
Richland	South Carolina	10,278	444	85
Orange	California	6,350	840	73
York	South Carolina	5,598	962	69
Pierce	Washington	3,582	1436	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,282	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	216	672	78
Davidson	Tennessee	183	743	76
Pierce	Washington	122	982	68
Orange	California	115	1023	67
York	South Carolina	43	1607	48

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.